import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				OmegaConf.load(lowercase				)
				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']
				__lowercase					     =				list(state_dict.keys()				)

				# extract state_dict for VQVAE
				__lowercase					     =				{}
				__lowercase					     =				'''first_stage_model.'''
				for key in keys:
								if key.startswith(lowercase				):
												__lowercase					     =				state_dict[key]

    # extract state_dict for UNetLDM
				__lowercase					     =				{}
				__lowercase					     =				'''model.diffusion_model.'''
				for key in keys:
								if key.startswith(lowercase				):
												__lowercase					     =				state_dict[key]

				__lowercase					     =				config.model.params.first_stage_config.params
				__lowercase					     =				config.model.params.unet_config.params

				__lowercase					     =				VQModel(**lowercase				).eval()
				vqvae.load_state_dict(lowercase				)

				__lowercase					     =				UNetLDMModel(**lowercase				).eval()
				unet.load_state_dict(lowercase				)

				__lowercase					     =				DDIMScheduler(
				    timesteps=config.model.params.timesteps				,     beta_schedule='''scaled_linear'''				,     beta_start=config.model.params.linear_start				,     beta_end=config.model.params.linear_end				,     clip_sample=lowercase				,     )

				__lowercase					     =				LDMPipeline(lowercase				,     lowercase				,     lowercase				)
				pipeline.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	Dict         =    argparse.ArgumentParser()
		parser.add_argument("""--checkpoint_path""", type=str, required=True)
		parser.add_argument("""--config_path""", type=str, required=True)
		parser.add_argument("""--output_path""", type=str, required=True)
		__a    :	Any         =    parser.parse_args()

		convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	List[Any]         =    logging.get_logger(__name__)

__a    :	List[Any]         =    {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Optional[Any]     					=		'''audio-spectrogram-transformer'''





			def __init__(  self ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-12 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=1_28 ,  **lowerCAmelCase__ ,  )	->   Optional[Any]:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__		)

							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				initializer_range
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				patch_size
							__lowercase					     =				qkv_bias
							__lowercase					     =				frequency_stride
							__lowercase					     =				time_stride
							__lowercase					     =				max_length
							__lowercase					     =				num_mel_bins